ism research study

8/2/2019 ISM Research Study

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Abstract

Over the last decade a new technology has begun to take hold in business. Its so new that its

significance is still difficult to evaluate. While many aspects of this technology are uncertain, itseems clear that it will move into the managerial scene rapidly, with definite and far reaching

impact on managerial organization.

Harold Lewitt and Thomas Whisler

Management in the 1980s

Harvard Business Review

NovemberDecember 1958

Some 30 years after

The publication of the article that began with this quotation, the impact of computer based

technology on business is still being discussed, debated, predicted and assessed. Indeed, although

the technology itself advances itself at beak neck speed, the questions surrounding the

technology have remained remarkably stagnant: How will organizational structures change?

How will current jobs and tasks change? Will some jobs disappear altogether? Which jobs will

be created? What net change in the number of jobs will occur? What can be expected as the

ripples of secondary and tertiary effectssocial, economic, and politicalare realized? How can

government, industry, academia, and labor best plan for (or direct) technological innovations

and their impact?

These questions constitute an imposing intellectual pie. The focus of this article is on but a small

piece of this pie: the impact of one technologyexpert systemson no skilled, semiskilled, and

skilled workers and first-line management over the coming years.

Assessing trends and future events is a forecasting problem. This research study approaches

estimating the impact of expert systems from this perspective. The terms knowledge based and

expert system denote a particular technology. As a technology, expert systems provide certain

benefits at certain costs and within certain limitations. The success of a technology depends at

least in part on these factors. It is instructive to recall both the benefits and limitations of expert

systems.


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Scope/ Applicability

Expert systems address areas where combinatory is enormous:

highly interactive or conversational applications, IVR, voice server, chatterbot fault diagnosis, medical diagnosis decision support in complex systems, process control, interactive user guide educational and tutorial software logic simulation of machines or systems knowledge management Constantly changing software.They can also be used in software engineering for rapid prototyping applications (RAD). Indeed,

the expert system quickly developed in front of the expert shows him if the future application

should be programmed.

Indeed, any program contains expert knowledge and classic programming always begins with an

expert interview. A program written in the form of expert system receives all the specific

benefits of expert system, among others things it can be developed by anyone without computer

training and without programming languages. But this solution has a defect: expert system runs

slower than a traditional program because he consistently "thinks" when in fact a classic software

just follows paths traced by the programmer.

Expert systems are designed to facilitate tasks in the fields of accounting, medicine, process

control, financial service, production, human resources, among others. Typically, the problem

area is complex enough that a more simple traditional algorithm cannot provide a proper

solution. The foundation of a successful expert system depends on a series of technical

procedures and development that may be designed by technicians and related experts. As such,

expert systems do not typically provide a definitive answer, but provide probabilistic

recommendations.
http://en.wikipedia.org/wiki/RADhttp://en.wikipedia.org/wiki/RADhttp://en.wikipedia.org/wiki/RADhttp://en.wikipedia.org/wiki/RAD


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An example of the application of expert systems in the financial field is expert systems for

mortgages. Loan departments are interested in expert systems for mortgages because of the

growing cost of labor, which makes the handling and acceptance of relatively small loans less

profitable. They also see a possibility for standardized, efficient handling of mortgage loan by

applying expert systems, appreciating that for the acceptance of mortgages there are hard and

fast rules which do not always exist with other types of loans. Another common application in

the financial area for expert systems are in trading recommendations in various marketplaces.

These markets involve numerous variables and human emotions which may be impossible to

deterministically characterize, thus expert systems based on the rules of thumb from experts and

simulation data are used. Expert system of this type can range from ones providing regional retail

recommendations, like Wishabi, to ones used to assist monetary decisions by financial

institutions and governments.

Another 1970s and 1980s application of expert systems, which we today would simply call AI,

was in computer games. For example, the computer baseball games Earl Weaver

Baseball and Tony La Russa Baseball each had highly detailed simulations of the game strategies

of those two baseball managers. When a human played the game against the computer, the

computer queried the Earl Weaver or Tony La Russa Expert System for a decision on what

strategy to follow. Even those choices where some randomness was part of the natural system

(such as when to throw a surprise pitch-out to try to trick a runner trying to steal a base) were

decided based on probabilities supplied by Weaver or La Russa. Today we would simply say that

"the game's AI provided the opposing manager's strategy".


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Introduction

Artificial Intelligence (AI) is the area of computer science focusing on creating machines that

can engage on behaviors that humans consider intelligent. The ability to create intelligentmachines has intrigued humans since ancient times and today with the advent of the computer

and 50 years of research into AI programming techniques, the dream of smart machines is

becoming a reality. Researchers are creating systems which can mimic human thought,

understand speech, beat the best human chess player, and countless other feats never before

possible.

In artificial intelligence, an expert system is a computer system that emulates the decision-

making ability of a human expert. Expert systems are designed to solve complex problems by

reasoning about knowledge, like an expert, and not by following the procedure of a developer as

is the case in conventional programming. An expert system has a unique structure, different from

traditional programs. It is divided into two parts, one fixed, independent of the expert system: the

inference engine, and one variable: the knowledge base. To run an expert system, the engine

reasons about the knowledge base like a human.

A computer application that performs a task that would otherwise be performed by a human

expert. For example, there are expert systems that can diagnose human illnesses, make financial

forecasts, and schedule routes for delivery vehicles. Some expert systems are designed to take

the place of human experts, while others are designed to aid them.

Expert systems are part of a general category of computer applications known as artificial

intelligence. To design an expert system, one needs a knowledge engineer, an individual who

studies how human experts make decisions and translates the rules into terms that

a computer can understand.

It is basically is a computer program that simulates the judgment and behavior of a human or an

organization that has expert knowledge and experience in a particular field. Typically, such a

system contains a knowledge base containing accumulated experience and a set of rules for

applying the knowledge base to each particular situation that is described to the program.


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Sophisticated expert systems can be enhanced with additions to the knowledge base or to the set

of rules.

An expert system typically consists of four major components:

1. Knowledge Base. This is the knowledge in the expert system, coded in a form that the system

can use. It is developed by some combination of humans (for example, a knowledge engineer)

and an automated learning system (for example, one that can learn through the analysis of good

examples ofan experts performance).

2. Problem Solver. This is a combination of algorithms and heuristics designed to use the

Knowledge Base in an attempt to solve problems in a particular field.

3. Communicator. This is designed to facilitate appropriate interaction both with the developers

of the expert system and the users of the expert system.


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4. Explanation and Help. This is designed to provide help to the user and also to provide

detailed explanations of the what and why of the expert systems activities as it works to solve

problem.

It is very important to understand the narrow specialization of the typical expert system. An

expert system designed to determine whether a person applying for a loan is a good loan risk

cannot diagnose infectious diseases, and vice versa. An expert system designed to help a lawyer

deal with case law cannot help a literature professor analyze poetry.

Researchers in AI often base their work on a careful study of how humans solve

problems and on human intelligence. In the process of attempting to develop effective AI

systems, they learn about human capabilities and limitations. One of the interesting things to

come out of work on expert systems is that within an area of narrow specialization, a humanexpert may be using only a few hundred to a few thousand rules.

Expert Systems Characteristics

By definition, an expert system is a computer program that simulates the thought process of a

human expert to solve complex decision problems in a specific domain. This chapter addresses

the characteristics of expert systems that make them different from conventional programming

and traditional decision support tools. The growth of expert systems is expected to continue forseveral years. With the continuing growth, many new and exciting applications will emerge. An

expert system operates as an interactive system that responds to questions, asks for clarification,

makes recommendations, and generally aids the decision-making process. Expert systems

provide expert advice and guidance in a wide variety of activities, from computer diagnosis to

delicate medical surgery.

An expert system may be viewed as a computer simulation of a human expert. Expert systems

are an emerging technology with many areas for potential applications. Past applications range

from MYCIN, used in the medical

field to diagnose infectious blood diseases, to XCON, used to configure computer systems. These

expert systems have proven to be quite successful. Most applications of expert systems will fall

into one of the following categories:


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Interpreting and identifying

Predicting

Diagnosing

Designing

Planning

Monitoring

Debugging and testing

Instructing and training

Controlling

Applications that are computational or deterministic in nature are not good candidates for expert

systems. Traditional decision support systems such as spreadsheets are very mechanistic in the

way they solve problems. They operate under mathematical and Boolean operators in their

execution and arrive at one and only one static solution for a given set of data. Calculationintensive applications with very exacting requirements are better handled by traditional decision

support tools or conventional programming. The best application candidates for expert systems

are those dealing with expert heuristics for solving problems. Conventional computer programs

are based on factual knowledge, an indisputable strength of computers. Humans, by contrast,

solve problems on the basis of a mixture of factual and heuristic knowledge. Heuristic

knowledge, composed of intuition, judgment, and logical inferences, is an indisputable strength

of humans. Successful expert systems will be those that combine facts and heuristics and thus

merge human knowledge with computer power in solving problems.

The Need for Expert Systems

Expert systems are necessitated by the limitations associated with conventional human decision-

making processes, including:


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1. Human expertise is very scarce.

2. Humans get tired from physical or mental workload.

3. Humans forget crucial details of a problem.

4. Humans are inconsistent in their day-to-day decisions.

5. Humans have limited working memory.

6. Humans are unable to comprehend large amounts of data quickly.

7. Humans are unable to retain large amounts of data in memory.

8. Humans are slow in recalling information stored in memory.

9. Humans are subject to deliberate or inadvertent bias in their actions.

10. Humans can deliberately avoid decision responsibilities.

11. Humans lie, hide, and die.

Coupled with these human limitations are the weaknesses inherent in conventional programming

and traditional decision-support tools. Despite the mechanistic power of computers, they have

certain limitations that impair their effectiveness in implementing human-like decision processes.

Conventional programs:

1. Are algorithmic in nature and depend only on raw machine power

2. Depend on facts that may be difficult to obtain

3. Do not make use of the effective heuristic approaches used by human experts

4. Are not easily adaptable to changing problem environments

5. Seek explicit and factual solutions that may not be possible


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Benefits of Expert Systems

Expert systems offer an environment where the good capabilities of humans and the power of

computers can be incorporated to overcome many of the limitations discussed.

1. Increase the probability, frequency, and consistency of making good decisions

2. Help distribute human expertise

3. Facilitate real-time, low-cost expert-level decisions by the non expert

4. Enhance the utilization of most of the available data

5. Permit objectivity by weighing evidence without bias and without regard for the users

personal and emotional reactions

6. Permit dynamism through modularity of structure

7. Free up the mind and time of the human expert to enable him or her to concentrate on more

creative activities

8. Encourage investigations into the subtle areas of a problem

Expert Systems Are For Everyone. No matter which area of business one is engaged in, expert

systems can fulfill the need for higher productivity andreliability of decisions. Everyone can find

an application potential in the field of expert systems. Contrary to the belief that expert systems

may pose a threat to job security, expert systems can actually help to create opportunities for new

job areas. Presented below are some areas that hold promise for new job opportunities:

Basic research

Applied research

Knowledge engineering

Inference engine development


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Consulting (development and implementation)

Training

Sales and marketing

Passive or active end user

An active user is one who directly uses expert systems consultations to obtain recommendations.

A passive user is one who trusts the results obtained from expert systems and supports the

implementation of those results.

TYPES OF EXPERT SYSTEMS

There are many different types of expert systems. The following list describes the various types.

Diagnosis. Diagnosis types of expert systems are used to recommend remedies to illnesses,

trouble-shoot electronic or mechanical problems or as debugging tools.

Repair. Expert systems that define repair strategies are also very common. As well as

diagnosing the problem they can suggest a plan for the repair of the item. The repair plan

typically contains a scheduling structure and some control structure to validate the repair process.Such systems have been employed in the automotive repair field and similar areas.

Instruction. Instructional expert systems have been used for individualized training or

instruction in a particular field. The system presents material in an order determined by its

evaluation of the users ability and current knowledge and monitors the progress of the student,

altering the sequence depending on this progress.

Interpretation. Interpretive expert systems have the ability to analyze data to determine its

significance or usefulness. The knowledge base often contains models of real world situations

which it compares to its data. These are often used in exploration for mineral, gas and oil

deposits as well as in surveillance, image analysis and speech understanding.


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Prediction.Predictive expert systems are used as a method to guess at the possible outcomes

of observed situations, usually providing a probability factor. This is used often in weather

forecasting.

Design and Planning. This allows experts to quickly develop solutions that save time. These

systems do not replace experts but act as a tool by performing tasks such as costing, building

design, material ordering and magazine design.

Monitoring and Control. In certain applications expert systems can be designed to monitor

operations and control certain functions. These are particularly useful where speed of decision

making is vitally important, for example in the nuclear energy industry, air traffic control and the

stock market.

Classification/Identification. These systems help to classify the goals in the system by the

identification of various features (these can by physical or non-physical) For example various

types of animals are classified according to attributes such as habitat, feeding information, color,

breeding information, relative size etc. These systems can be used by bird watchers, fishing

enthusiasts, animal rescue shelters (to match animals to prospective owners) to name a few.


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Historic Development

Expert systems were introduced by researchers in the Stanford Heuristic Programming Project,

including the "father of expert systems" Edward Feigenbaum, with

the Dendral and Mycin systems. Principal contributors to the technology were Bruce Buchanan,

Edward Shortliffe, Randall Davis, William vanMelle, Carli Scott and others at Stanford. Expert

systems were among the first truly successful forms of AI software.

Research is also very active in France, where researchers focus on the automation of reasoning

and logic engines. The French Prolog computer language, designed in 1972, marks a real

advance over expert systems like Dendral or Mycin: it is a shell, that's to say a software structure

ready to receive any expert system and to run it. It integrates an engine using First-Order logic,

with rules and facts. It's a tool for mass production of expert systems and was the first

operational declarative language, later becoming the best selling IA language in the world.

However Prolog is not particularly user friendly and is an order of logic away from human logic .

In the 1980s, expert systems proliferated as they were recognized as a practical tool for solving

real-world problems. Universities offered expert system courses and two thirds of the Fortune

1000companies applied the technology in daily business activities. Interest was international

with the Fifth Generation Computer Systems project in Japan and increased research funding in

Europe. Growth in the field continued into the 1990s.

The development of expert systems was aided by the development of the symbolic processing

languages Lisp and Prolog. To avoid re-inventing the wheel, expert system shells were created

that had more specialized features for building large expert systems.

In 1981 the first IBM PC was introduced, with MS-DOS operating system. Its low price started

to multiply users and opened a new market for computing and expert systems. In the 80's the

image of IA was very good and people believed it would succeed within a short time. Many

companies began to market expert systems shells from universities, renamed "generators"

because they added to the shell a tool for writing rules in plain language and thus, theoretically,

allowed to write expert systems without a programming language nor any other software. The

best known: Guru (USA) inspired by Mycin, Personal Consultant Plus (USA), Nexpert Object


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(developed by Neuron Data, company founded in California by three French), Genesia

(developed by French public company Electricit de France and marketed by Steria), VP Expert

(USA). But eventually the tools were only used in research projects. They did not penetrate the

business market, showing that AI technology was not mature.

In 1986, a new expert system generator for PCs appeared on the market, derived from the French

academic research: Intelligence Service sold by GSI-TECSI software company. This software

showed a radical innovation: it used propositional logic ("Zeroth order logic") to execute expert

systems, reasoning on a knowledge base written with everyday language rules, producing

explanations and detecting logic contradictions between the facts. It was the first tool showing

the AI defined by Edward Feigenbaum in his book about the Japanese Fifth

Generation, Artificial Intelligence and Japan's Computer Challenge to the World (1983): "The

machines will have reasoning power: they will automatically engineer vast amounts of

knowledge to serve whatever purpose humans propose, from medical diagnosis to product

design, from management decisions to education", "The reasoning animal has, perhaps

inevitably, fashioned the reasoning machine", "the reasoning power of these machines matches

or exceeds the reasoning power of the humans who instructed them and, in some cases, the

reasoning power of any human performing such tasks". Intelligence Service was in fact

"Pandora" (1985), a software developed for their thesis by two academic students of Jean-Louis

Laurire, one of the most famous and prolific French AI researcher. Unfortunately, as this

software was not developed by his own IT developers, GSI-TECSI was unable to make it evolve.

Sales became scarce and marketing stopped after a few years.


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Examination of Current Issues

Technical issues:

On the technical side, there is the problem of the size of the database and using it efficiently. If

the system consists of several thousand rules, it takes a very powerful control program to

produce any conclusions in a reasonable amount of time. If the system also has a large quantity

of information in the working memory, this will also slow things down unless you have a very

good indexing and search system.

Data integrity:

A second problem that comes from a large database is that as the number of rules increases the

conflict set also becomes large so a good conflict resolving algorithm is needed if the system is

to be usable.

Accountability and responsibility issues:

Another problem that appears is that of responsibility. Take, for example, a system used by a

doctor that is designed to administer drugs to patients according to their needs and that it must

first determine what is wrong with them, very much like the prescribing work of a GP. If the

system causes someone to take the wrong medicine and the person is harmed, who is legally

responsible? Some would say the health authority who allowed the doctor to use the system,

others would say the doctor, others the suppliers of the Expert System. A problem is produced

that is not at all a trivial one. Think about the implications of using Expert Systems in other

scenarios.

Cannot substitute human expertise:

A more obvious problem is that of gathering the rules. Human experts are expensive and are not

extremely likely to want to sit down and write out a large number of rules as to how they come to

their conclusions. More to the point, they may not be able to. Although they will usually follow a

logical path to their conclusions, putting these into a set of IF ... THEN rules may actually be


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very difficult and maybe impossible.

It is quite possible that many human experts, though starting off in their professions with a set of

rules, learn to do their job through experiential knowledge and 'just know' what the correct

solution is. Again they may have followed a logical path, but mentally they may have 'skipped

some steps' along the way to get there. An Expert System cannot do this and needs to know the

rules very clearly.

What may be a way round this problem is to enable Expert Systems to learn as they go, starting

off with a smaller number of rules but given the ability to deduce new rules from what they know

and what they 'experience'.

Problem of Explanation and Control:

The system would appear chaotic and be so inefficient as to be unusable if the expert's rules had

to be rederived from first principles of the domain (and logic) for each application. In a sense,

using expert rules saves the system the chore of learning the deductions do not need to be

repeated. However, these systems perform in more limited domains and are harder to extend; for

example, can any of the existing expert systems for medical diagnosis and treatment

recommendation be extended, using their current knowledge base or a slight augmentation, to do

preventative health care in the same area of medicine? Its not possible.

Compilation and compression are not unmitigated blessings because the expert's rule is usually

derived from experience rather than being model based. Thus, although these rules usually

produce correct behavior, they also have the potential to produce incorrect or inconsistent results.

The rules are plausible and work a high percentage of the time; this is why the expert uses them.

However, when they fail, the human expert knows enough to recognize this fact and find out

why. He retreats to a better-grounded model (one based upon more general principles) and

determines where the compiled inference chain failed and why it is not applicable in this

particular case.


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Security threats and issues:

Computer risk exposures and security in general are reviewed, and factors suggesting that expert

system security are a unique and crucial problem. Security requirements and threats associated

with the unique characteristics of expert systems are investigated. They include technical aspects

of knowledge (certainty factors, symbolic information and special fixes), structure (user

interface, knowledge base, inference engine and database), design methodology (prototyping)

and the current delivery environment (e.g., PCs and expert system shells). The security of

working expert systems is important as it affects the confidentiality, integrity and authenticity of

the data and knowledge. Rationales for the current apparent lack of expert system security are

providing opportunities to the hackers and crackers to transfer, manipulate, modify and eliminate

the knowledge, codes and rules. The impact of possible security controls on expert system users

and developers is worth assessing.

Legal and ethical issues:

Increasing specialization and the growth of automated advice-delivery systems are creating new

problems in legal responsibility and ethical behavior. Engineering, planning, legal, and medical

workers can expect early encounters with these difficulties, which are essentially concerned with

a new interpretation of 'due care' and of 'professional liability'. The precipitating factor in this

debate is the emergence of usable 'expert' systems, which embody judgmental and operational

knowledge, and are often designed to mimic the behavior (if not the public pronouncements) of

acknowledged experts in the field. The task of the knowledge engineer and of the professional

worker using or expecting others to use such automated advisory systems raises ethical problems

both for individuals and for professional and learned societies.


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The organizational impact of expert systems

ForecastingForecasting requires predicting what will happen and when it will happen along with an

associated statement of confidence. Forecasting horizon is inversely related to forecasting

accuracy. This result is intuitive.

The likelihood of unforeseen events increases as the time period over which the forecast is made

increases .Qualitative forecasting, that is , forecasting based on human judgment, is difficult, as

described previously, due to human biases and the limitations of human information processing.

The results may often tend to be incorrect and the whole planning process will go in vain as it is

directly dependent on an effective forecast. The expert systems help organizations, especially the

top line managers and top notch executives to forecast effectively using the expertise and

knowledge of these systems. They help in forecasting for long time horizons, new markets,

changing preferences, demands and tastes of the consumers.

Advanced Manufacturing TechnologyWhat is it about the Japanese that has made their manufacturing so successful? The growthmarket forecasts appearing there. The answer to this more recent riddle is more forthcoming

and can be summarized under the headings technology, just-in-time (JIT) scheduling policies,

and participatory management. The technology includes robotic sand computer-aided design,

manufacture, and process planning. The technology itself, however, does not account for the

success of the Japanese. Linked to the technology is a set of policies that allow for its maximal

utilization. These automated and technology driven processes definitely use the expert system

technology and procedures.

Decision makingAn organizational decision making framework that has received substantial amount of attention

in expert systems are strategic planning, management control and task (operational) controlon


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one dimension and the structure of the problem under consideration- structured, semi

structured and unstructuredon the other dimension.

The effectiveness of decision making can be greatly improved if the problem is suitable for

expert system treatment. This is confirmed by a survey conducted by Fried who cited the

following benefits in successful ESs:

Improved decisions by nonexperts, More consistent decisions, Reduced response time, Improved training, and Cost reduction.

Such benefits indicate expert systems impact decision making.

Overall organizational effectiveness and efficiencyExpert systems can be developed to give advice on how to increase efficiency and effectiveness

in operations, processes of the organization, as well as to aid in their evaluation. There are a

number of factors like human resources, processes, efficient delivery system, customer

relationship management, supply chain management, etc. that are related to the firms success

and they can form the basis of an expert system that would evaluate organizational performance.

Organizational rolesThe existence of an expert indicates that there are distinct organizational roles for the expert.

There is a number of role specializations, including the problem and the method or process by

which the work is done.

The increase in the number of problems can be solved by the use of expert systems will save

managers a considerable amount of time, freeing them from routine tasks. An expert system, for

example, can save the time being spent on checking manuals and directories. This will allow

managers the opportunity to engage in more creative activities with more quality time. The


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specialized nature of AI/ES tools and the difficulty of knowledge acquisitions has been

instrumental in creating the new role of the knowledge engineers. At the same time the potential

was of use of ES shells could turn the user into an ES builderprogrammer.


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Findings

The impact of a technology is a function of policies regarding how the technology is to be used.

These policy choices are a function of the business environment; the culture in which the

technology is introduced; and, indeed, the technology itself. The first of these guidelines suggests

that appropriate further work in impact assessment for expert systems should include

fundamental market research. Although the characteristics of a problem that make it amenable

to expert system solution have been isolated (Dym 1987; Bobrow, Mittal, and

Stefik 1986; Weitz and DeMeyer 1989), the purpose of this market research should be to

realistically determine the number of organizations with these problems and whether the costs,

benefits, and potential strategic advantage afforded by an expert system solution support or

discourage the likelihood that expert system technology will be applied.

Future research in the area of technology impact should be directed toward measuring and

quantifying the factors in the technology.


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Recommendations and Conclusion


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Bibliography/ References

Dym, C. L. 1987. Issues in the Design and Implementation of Expert Systems. ArtificialIntelligence for Engineering Design, Analysis, and Manufacturing (AI EDAM) 1(1): 37

46

Walton, R., and Susman, G. 1987. People Policy forthe New Machines. HarvardBusiness Review 65(2): 98107

Darlington, Keith (2000). The Essence of Expert Systems.Pearson Education. Ignizio, James (1991).Introduction to Expert Systems. McGraw-Hill Companies. Jackson, Peter (1998),Introduction To Expert Systems (3 ed.), Addison Wesley, p. 2,

2.5 USER INTERFACE
http://en.wikipedia.org/wiki/Pearson_Educationhttp://en.wikipedia.org/wiki/Pearson_Educationhttp://en.wikipedia.org/wiki/Pearson_Educationhttp://en.wikipedia.org/wiki/Pearson_Education


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The initial development of an expert system is performed by the expert and

the knowledge engineer. Unlike most conventional programs, in which only

programmers can make program design decisions, the design of large expert

systems is implemented through a team effort. A consideration of the needs

of the end user is very important in designing the contents and user interface

of expert systems.2.5 USER INTERFACE 23

2.5.1 Natural Language

The programming languages used for expert systems tend to operate in a

manner similar to ordinary conversation. We usually state the premise of a

problem in the form of a question, with actions being stated much as when

we verbally answer the question, that is, in a natural language format. If,

during or after a consultation, an expert system determines that a piece of its

data or knowledge base is incorrect or is no longer applicable because the

problem environment has changed, it should be able to update the knowledge

base accordingly. This capability would allow the expert system to converse

in a natural language format with either the developers or users.

Expert systems not only arrive at solutions or recommendations, but can

give the user a level of confidence about the solution. In this manner, an


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expert system can handle both quantitative and qualitative factors when analyzing problems.

This aspect is very important when we consider how inexact most input data are for day-to-day

decision making. For example, the

problems addressed by an expert system can have more than one solution or,

in some cases, no definite solution at all. Yet the expert system can provide

useful recommendations to the user just as a human consultant might do.

2.5.2 Explanations Facility in Expert Systems

One of the key characteristics of an expert system is the explanation facility.

With this capability, an expert system can explain how it arrives at its conclusions. The user can

ask questions dealing with the what, how, and why

aspects of a problem. The expert system will then provide the user with a

trace of the consultation process, pointing out the key reasoning paths followed during the

consultation. Sometimes an expert system is required to

solve other problems, possibly not directly related to the specific problem at

hand, but whose solution will have an impact on the total problem-solving

process. The explanation facility helps the expert system to clarify and justify

why such a digression might be needed.

2.5.3 Data Uncertainties

Expert systems are capable of working with inexact data. An expert system

allows the user to assign probabilities, certainty factors, or confidence levels


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to any or all input data. This feature closely represents how most problems

are handled in the real world. An expert system can take all relevant factors

into account and make a recommendation based on the best possible solution

rather than the only exact solution.

2.5.4 Application Roadmap

The symbolic processing capabilities of AI technology lead to many potential

applications in engineering and manufacturing. With the increasing sophisti-cation of AIl

techniques, analysts are now able to use innovative methods to

provide viable solutions to complex problems in everyday applications. Figure

2.5 presents a structural representation of the application paths for artificial

intelligence and expert systems.

2.5.5 Symbolic Processing

Contrary to the practice in conventional programming, expert systems can

manipulate objects symbolically to arrive at reasonable conclusions to a problem scenario. The

object drawings in this section are used to illustrate the

versatility of symbolic processing by using the manipulation of objects to

convey information.

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